---
title: "Interactive Dashboard: Role of Females in Movie Industry"
author: Dinara Talypova
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    social: menu
    source_code: embed
---

Column {data-width=650}
-----------------------------------------------------------------------

### Interactive Chart

```{r}
#install.packages("shiny")
library(shiny)
library(ggplot2)
library(plotly)
library(dplyr)
 library(dplyr)
  library(foreach)
  library(ggplot2)
  library(likert)
  library(psych)
  library(stringr)
  library(ggsignif)
  library(scales)
  library(hrbrthemes)
  library(viridis)
  library(tidyr)
  library(outliers)
  library(coin)
  library(rstatix)
library(summarytools)
library(scales)
library(summarytools)
library("stringr")
library(ggcorrplot)
library(jsonlite)
library(viridisLite)


```

```{r}
joined = read.csv("https://raw.githubusercontent.com/tadiri/female_movies_vds/main/joined_female_movies.csv")
sum_bechdel <- joined %>%
  group_by(year, director_gender) %>%
  get_summary_stats(bechdel, type = "mean") %>% 
  na.omit() %>% 
  dplyr::rename(bechdel_mean = "mean")
sum_bechdel$bechdel_mean <- round(sum_bechdel$bechdel_mean, 2)

sum_imdb <- joined %>%
  group_by(year, director_gender) %>%
  get_summary_stats(imdb_score, type = "mean") %>% 
  na.omit() %>% 
  dplyr::rename(imdb_mean = "mean")
sum_imdb$imdb_mean <- round(sum_imdb$imdb_mean, 2)

sum_gross <- joined %>%
  group_by(year, director_gender) %>%
  get_summary_stats(gross, type = "mean") %>% 
  na.omit() %>% 
  dplyr::rename(gross_mean = "mean")
sum_gross$gross_mean <- round(sum_gross$gross_mean, 2)

sum <- merge(sum_bechdel, sum_imdb, by = c("year", "director_gender"), all = TRUE)
sum <- merge(sum, sum_gross, by = c("year", "director_gender"), all = TRUE)


selectInput("parameter", "Select Parameter:", choices = c("bechdel_mean", "imdb_mean", "gross_mean"))

# colors <- viridis(5)
# print(colors)
```

```{r}

renderPlotly({
  selected_parameter <- input$parameter
  
linear <- sum %>%
    ggplot(aes(x = year, y = get(selected_parameter), group = director_gender, color = director_gender)) +
    geom_line() +
    scale_color_manual(values = c("#440154", "#21908C")) +
    theme_bw()
  
  ggplotly(linear)
})

plotlyOutput("myPlot")


```

Column {data-width=350}
-----------------------------------------------------------------------

### Chart B

```{r}
corr <- round(cor(joined %>% dplyr::select(year:imdb_votes, budget:oscar_nominated), use ="na.or.complete", method = c("spearman")), 2)
p.mat <- cor_pmat(joined  %>% dplyr::select(year:imdb_votes, budget:oscar_nominated))
diverging_colors <- c(
   "#d12ed1a5", 
  "#F7F7F7", "#21908C"
)

ggplot_corr <- ggcorrplot(
  corr,
  p.mat = p.mat,
  hc.order = FALSE,
  type = "lower",
  lab = TRUE,
  lab_size = 3.5,     
  insig = "pch",
  colors = diverging_colors)

interactive_plot <- ggplotly(ggplot_corr)

```

```{r}
renderPlotly({ interactive_plot })

```

### Chart C

```{r}

```